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# LIBRARY IMPORTS
import sys
import streamlit as st
from streamlit import cli as stcli
import plotly.express as px
import pandas as pd
# FUNCTIONS
def data_import():
"""
Imports 2019 U.S. state energy data from https://www.eia.gov/state/.
"""
df = pd.read_csv('SelectedStateRankingsData.csv')
return df
def data_transformation(df):
"""
Transforms the data to prepare for analysis and visualization.
"""
# Drop last column
df_tr = df.iloc[:, :-1]
# Get list of original column names you want to change
col_names = list(df_tr.columns)
col_names.remove('State')
# Create list of revised column names
col_names2 = [
'Production (U.S. Share %)',
'Production (Rank)',
'Consumption per Capita (Million BTU)',
'Consumption per Capita (Rank)',
'Expenditures per Capita ($)',
'Expenditures per Capita (Rank)'
]
# Create dictionary of original and revised column names
col_names_new = dict(zip(col_names, col_names2))
# Rename columns using previously created dictionary
df_tr = df_tr.rename(columns=col_names_new)
return df_tr, col_names_new
def display_header():
"""
Displays the header section of the app.
"""
st.title("Energy Production, Consumption, & Expenditure By State")
st.subheader("This app displays data mined from the EIA.gov website for 2019 energy production, consumption, "
"and expenditure by state. Use the dropdown menu to filter the heatmap, which is interactive so you "
"can hover over each state to view the filtered information.")
st.write("A separate Qlik dashboard to explore this data can be viewed here: [link](https://yq99nw4o7qdge1s.us.qlikcloud.com/single/?appid=68edb70d-68b9-4050-b956-34c12bc2460c&sheet=37d34302-8565-4551-afd2-abd2dcffa243&theme=horizon&opt=ctxmenu,currsel&select=clearall)")
def display_filter(col_names):
# Take values from col_names dictionary and convert to a list
col_names_list = list(col_names.values())
# Create dropdown menu to filter data for chart
filter_selection = st.selectbox('Filter the data', col_names_list)
return filter_selection
# noinspection PyShadowingBuiltins
def display_chart(df_tr, filter):
"""
Displays various charts of the data.
"""
# Determine filter value in order to set proper color scale for chart
if 'rank' in filter.lower():
color_scale = 'plasma_r'
else:
color_scale = 'plasma'
#te
# Create chart
fig = px.choropleth(df_tr,
locations='State',
color=filter,
locationmode='USA-states', # Set to plot as US States
title='2019 U.S. Energy {filter}'.format(filter=filter),
color_continuous_scale=color_scale,
)
# Add title and set USA as boundary for map
fig.update_layout(title_x=0.4,
geo_scope='usa', # Plot only the USA instead of globe
height=800,
width=1000
)
# Display chart using Streamlit
st.plotly_chart(fig)
def main():
"""
Main function for the app which calls all other functions to display the app.
"""
# IMPORT DATA
energy_rankings = data_import()
# TRANSFORM DATA
energy_rankings_tr, col_names_new = data_transformation(energy_rankings)
# DISPLAY DATA
display_header()
filter_selection = display_filter(col_names_new)
display_chart(energy_rankings_tr, filter_selection)
if __name__ == '__main__':
if st._is_running_with_streamlit:
main()
else:
sys.argv = ['streamlit', 'run', sys.argv[0]]
sys.exit(stcli.main())
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